Introduction

Hui Lin @Netlify

Ming Li @Amazon

2019-02-10

Schedule

Topic Time
Introduction 8:00 - 8:45
Deep Learning 1 8:45 - 9:45
Break 9:45 - 10:00
Deep Learning 2&3 10:00 - 12:00
Lunch break 12:00 - 13:30
Deep Learning Hands on Session 13:30 - 15:00
Break 15:00 - 15:15
Big Data Cloud Platform Lecture and hands on 15:15 - 16:00
Analytical dashboard and report 16:00 - 16:30
Soft Skill and Project Cycle 16:30 - 17:15
Q&A 17:15 - 17:30

Slides

https://github.com/happyrabbit/DataScienceWorkshop2019

The term no one really defined

Data science is the discipline of making data useful. Ok…so what is it?

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What is “hard-core pornography”?

What is “hard-core pornography”?

“I know it when I see it. (Potter Stewart)”

Three tracks of data science

(It is a group work from https://github.com/brohrer/academic_advisory/blob/master/authors.md !)

Engineering

  1. Data environment: data storage, Kafka platform, Hadoop and Spark cluster etc.

  2. Data management: parsing the logs, web scraping, API queries, and interrogating data streams.

  3. Production: integrate model and analysis into the production system

Engineering - Production

Data Pipeline

Analysis

  1. Domain knowledge

  2. Exploratory analysis

  3. Story telling

Modeling

  1. Supervised learning

  2. Unsupervised learning

  3. Customized model development

General Process of Modeling/Analytics

Three tracks of data science

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Three tracks of data science

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Three tracks of data science

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Data Science Curriculum Roadmap

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What can (should) data science do?

Data Science Hierarchy of Needs

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Data Science Types v.s Needs

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Data Science Types v.s Needs

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Data Science Types v.s Needs

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Data Science Types v.s Needs

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Types of Questions (Modeling/Analytics)

Types of Questions (Modeling/Analytics)